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Enhanced visual data mining process for dynamic decision-making

Abstract : Data mining has great potential in extracting useful knowledge from large amount of temporal data for dynamic decision-making. Moreover, integrating visualization in data mining, known as visual data mining, allows combining the human ability of exploration with the analytical processing capacity of computers for effective problem solving. To design and develop visual data mining tools, an appropriate process must be followed. In this context, the goal of this paper is to enhance existing visualization processes by adapting it under the temporal dimension of data, the data mining tasks and the cognitive control aspects. The proposed process aims to model the visual data mining methods for supporting the dynamic decision-making. We illustrate the steps of our proposed process by considering the design of the visualization of the temporal association rules technique. This technique was developed to assist physicians to fight against nosocomial infections in the intensive care unit. Actually, an evaluation study in Situ was performed to assess the automatic prediction results as well as the visual representations. At the end, the test of the efficiency of our process using utility and usability evaluation shows satisfactory.
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https://hal-uphf.archives-ouvertes.fr/hal-03280476
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Submitted on : Wednesday, July 7, 2021 - 1:14:56 PM
Last modification on : Tuesday, May 17, 2022 - 10:44:24 AM

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Hela Ltifi, Emna Benmohamed, Christophe Kolski, Mounir Ben Ayed. Enhanced visual data mining process for dynamic decision-making. Knowledge-Based Systems, Elsevier, 2016, 112, pp.166-181. ⟨10.1016/j.knosys.2016.09.009⟩. ⟨hal-03280476⟩

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